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dc.contributor.authorSánchez, Marta
dc.contributor.authorSánchez Peña, Enric
dc.contributor.authorBermúdez López, Marcelino
dc.contributor.authorTorres, Gerard
dc.contributor.authorFarràs-Sallés, Cristina
dc.contributor.authorPamplona Gras, Reinald
dc.contributor.authorCastro-Boqué, Eva
dc.contributor.authorValdivielso Revilla, José Manuel
dc.contributor.authorPurroy Garcia, Francisco
dc.contributor.authorMartínez Alonso, Montserrat
dc.contributor.authorGodoy i García, Pere
dc.contributor.authorMauricio Puente, Dídac
dc.contributor.authorFernández i Giráldez, Elvira
dc.contributor.authorHernández García, Marta
dc.contributor.authorRius, Ferran
dc.contributor.authorLecube Torelló, Albert
dc.contributor.otherILERVAS Project Collaborators
dc.date.accessioned2021-05-25T10:34:57Z
dc.date.available2021-05-25T10:34:57Z
dc.date.issued2021-03-23
dc.identifier.issn2072-6643
dc.identifier.urihttp://hdl.handle.net/10459.1/71310
dc.description.abstractPrediabetes is closely related to excess body weight and adipose distribution. For this reason, we aimed to assess and compare the diagnostic usefulness of ten anthropometric adiposity indices to predict prediabetes. Cross-sectional study with 8188 overweight subjects free of type 2 diabetes from the ILERVAS project (NCT03228459). Prediabetes was diagnosed by levels of glycated hemoglobin (HbA1c). Total body adiposity indices [BMI, Clínica Universidad de Navarra-Body Adiposity Estimator (CUN-BAE) and Deurenberg's formula] and abdominal adiposity (waist and neck circumferences, conicity index, waist to height ratio, Bonora's equation, A body shape index, and body roundness index) were calculated. The area under the receiver-operating characteristic (ROC) curve, the best cutoff and the prevalence of prediabetes around this value were calculated for every anthropometric index. All anthropometric indices other than the A body adiposity were higher in men and women with prediabetes compared with controls (p < 0.001 for all). In addition, a slightly positive correlation was found between indices and HbA1c in both sexes (r ≤ 0.182 and p ≤ 0.026 for all). None of the measures achieved acceptable levels of discrimination in ROC analysis (area under the ROC ≤ 0.63 for all). Assessing BMI, the prevalence of prediabetes among men increased from 20.4% to 36.2% around the cutoff of 28.2 kg/m2, with similar data among women (from 29.3 to 44.8% with a cutoff of 28.6 kg/m2). No lonely obesity index appears to be the perfect biomarker to use in clinical practice to detect individuals with prediabetes.
dc.description.sponsorshipThis work was supported by grants from the Diputació de Lleida and Generalitat de Catalunya (2017SGR696 and SLT0021600250). CIBER de Diabetes y Enfermedades Metabólicas Asociadas and CIBER de Enfermedades Respiratorias are initiatives of the Instituto de Salud Carlos III.
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherMDPI
dc.relation.isformatofReproducció del document publicat a: https://doi.org/10.3390/nu13031002
dc.relation.ispartofNutrients, 2021, vol. 13, num. 3, p. 1002
dc.rightscc-by (c) Sánchez, Marta et al., 2021
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectAdiposity
dc.subjectBody composition
dc.subjectPrediabetes
dc.subjectGlycated hemoglobin
dc.subjectObesity
dc.titleClinical Usefulness of Anthropometric Indices to Predict the Presence of Prediabetes. Data from the ILERVAS Cohort
dc.typeinfo:eu-repo/semantics/article
dc.date.updated2021-05-25T10:34:57Z
dc.identifier.idgrec031262
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.identifier.doihttps://doi.org/10.3390/nu13031002


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cc-by (c) Sánchez, Marta et al., 2021
Except where otherwise noted, this item's license is described as cc-by (c) Sánchez, Marta et al., 2021